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The coronavirus disease 2019 (COVID-19) pandemic has led to a reimagining of many aspects of higher education, including how instructors interact with their students and how they encourage student participation. Text-based chatting during synchronous remote instruction is a simple form of student-student and student-instructor interaction. The importance of student participation has been documented, as have clear disparities in participation between those well-represented and those under-represented in science disciplines. Thus, we conducted an investigation into who is texting, what students are texting, and how these texts align with course content. We focused on two sections of a large-enrollment, introductory biology class offered remotely during Fall 2020. Using an analysis of in-class chatting, in combination with student survey responses, we find that text-based chatting suggests not only a high level of student engagement, but a type of participation that is disproportionately favored by women. Given the multiple lines of evidence indicating that women typically under-participate in their science courses, any vehicle that counters this trend merits further exploration. We conclude with suggestions for further research, and ideas for carrying forward text-based chatting in the post-COVID-19, in-person classroom. 

To achieve meaningful learning experiences in online classrooms, students must become self-regulated learners through the development of effective study habits. Currently, there is no set of recommendations to promote study habits in online biology learning environments. 

Abstract Advances in quantitative biology data collection and analysis across scales (molecular, cellular, organismal, and ecological) have transformed how we understand, categorize, and predict complex biological systems. This surge of quantitative data creates an opportunity to apply, develop, and evaluate mathematical models of biological systems and explore novel methods of analysis. Simultaneously, thanks to increased computational power, mathematicians, engineers and physical scientists have developed sophisticated models of biological systems at different scales. Novel modeling schemes can offer deeper understanding of principles in biology, but there is still a disconnect between modeling and experimental biology that limits our ability to fully realize the integration of mathematical modeling and biology. In this work, we explore the urgent need to expand the use of existing mathematical models across biological scales, develop models that are robust to biological heterogeneity, harness feedback loops within the iterative modeling process, and nurture a cultural shift towards interdisciplinary and cross-field interactions. Better integration of biological experimentation and robust mathematical modeling will transform our ability to understand and predict complex biological systems.